Group Predictions

Row

Win percentage for the week

Season Win Percentage

Games Correct

149

Games Picked

208

Number of predictions

116

Row

This Week’s Predictions
Game Prediction Winner Correct Correct Votes Correct Percent
1 Detroit Lions Detroit Lions Yes 97 0.8362
2 Philadelphia Eagles Philadelphia Eagles Yes 114 0.9828
3 Minnesota Vikings Minnesota Vikings Yes 112 0.9655
4 Pittsburgh Steelers Pittsburgh Steelers Yes 111 0.9569
5 Tennessee Titans Jacksonville Jaguars No 26 0.2241
6 Tampa Bay Buccaneers Tampa Bay Buccaneers Yes 111 0.9569
7 New Orleans Saints New Orleans Saints Yes 103 0.8879
8 Miami Dolphins Miami Dolphins Yes 110 0.9483
9 Seattle Seahawks Seattle Seahawks Yes 65 0.5603
10 Buffalo Bills Los Angeles Rams No 9 0.0776
11 San Francisco 49ers San Francisco 49ers Yes 74 0.6379
12 Kansas City Chiefs Kansas City Chiefs Yes 91 0.7845
13 Cincinnati Bengals Cincinnati Bengals Yes 86 0.7414

Individual Predictions

row

Individual Table

Individual Results
Week 14
Name Weekly # Correct Percent Weeks Picked Season Percent Adj Season Percent Season Trend
Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Week 13 Week 14
Gregory Brown 15 7 6 9 8 12 9 9 13 9 10 9 15 12 0.9231 14 0.6875 0.6875
Brittany Pillar NA NA NA NA NA 10 12 NA NA 10 11 10 16 11 0.8462 7 0.8081 0.4040
Matthew Blair NA NA NA NA NA 11 10 12 10 9 11 8 15 11 0.8462 9 0.7462 0.4797
Robert Gelo 14 8 9 9 8 13 13 11 12 10 10 9 12 11 0.8462 14 0.7163 0.7163
Bradley Hobson 13 7 8 11 7 13 10 10 11 8 NA 10 16 11 0.8462 13 0.6959 0.6462
Brian Hollmann NA NA NA 8 8 10 10 11 9 8 12 8 16 11 0.8462 11 0.6938 0.5451
Patrick Tynan 12 8 7 9 8 12 NA 12 12 10 10 6 16 11 0.8462 13 0.6891 0.6399
William Schouviller 12 7 9 9 11 13 10 9 NA 7 11 8 15 11 0.8462 13 0.6839 0.6350
Shaun Dahl 14 7 9 11 10 10 10 8 9 11 8 11 12 11 0.8462 14 0.6779 0.6779
Thomas Brenstuhl 9 8 NA 6 7 9 10 13 11 9 13 9 15 11 0.8462 13 0.6771 0.6287
Jennifer Bouland 13 8 10 7 8 11 10 11 9 9 12 10 NA 11 0.8462 13 0.6719 0.6239
Daniel Major 8 10 11 6 8 11 NA 10 10 10 11 9 14 11 0.8462 13 0.6684 0.6207
George Brown 14 7 8 7 6 11 10 12 9 12 11 8 12 11 0.8462 14 0.6635 0.6635
Earl Dixon 10 9 6 9 9 NA 11 10 9 9 10 10 15 11 0.8462 13 0.6598 0.6127
George Mancini 11 8 6 NA 8 6 12 NA 11 9 11 10 13 11 0.8462 12 0.6591 0.5649
Edward Ford 9 7 6 10 5 10 10 13 11 9 12 10 14 11 0.8462 14 0.6587 0.6587
Richard Beeghley 11 7 6 11 7 14 10 10 10 8 9 9 14 11 0.8462 14 0.6587 0.6587
Jared Kaanga 11 9 9 8 7 10 9 11 13 9 9 7 NA 11 0.8462 13 0.6406 0.5948
Cade Martinez 10 7 8 8 6 11 11 9 10 10 9 8 15 11 0.8462 14 0.6394 0.6394
Jeffrey Zornes 9 11 6 8 7 10 9 11 9 10 NA 8 15 11 0.8462 13 0.6392 0.5935
Christopher Mulcahy 11 9 7 8 NA 8 9 9 10 9 10 9 13 11 0.8462 13 0.6340 0.5887
Darvin Graham 12 7 6 9 8 11 9 NA 10 9 9 10 NA 11 0.8462 12 0.6307 0.5406
Rachel Follo 15 8 6 6 9 7 10 11 9 9 10 7 13 11 0.8462 14 0.6298 0.6298
Wayne Schofield 7 5 9 5 7 7 11 11 10 8 13 10 13 11 0.8462 14 0.6106 0.6106
Jack Wheeler 9 6 5 10 8 NA 9 9 10 8 10 7 15 11 0.8462 13 0.6031 0.5600
Gary Lawrence 10 6 5 5 7 9 9 10 9 9 11 8 16 11 0.8462 14 0.6010 0.6010
Robert Cunningham 14 9 10 12 8 12 11 11 12 9 11 10 NA 10 0.7692 13 0.7240 0.6723
Randolph Tidd 11 7 8 12 NA 12 11 12 13 9 11 7 16 10 0.7692 13 0.7165 0.6653
Aubrey Conn 13 7 10 9 8 12 12 9 13 9 10 11 16 10 0.7692 14 0.7163 0.7163
Michael Pacifico 13 8 7 9 9 12 12 10 14 9 11 10 14 10 0.7692 14 0.7115 0.7115
Nathan Brown 13 8 9 11 9 NA 10 11 14 9 10 10 14 10 0.7692 13 0.7113 0.6605
Chester Todd 13 8 8 8 9 13 13 10 9 9 11 9 15 10 0.7692 14 0.6971 0.6971
Keven Talbert 10 7 9 11 9 14 13 9 9 11 10 10 12 10 0.7692 14 0.6923 0.6923
Christopher Sims 11 9 10 8 7 10 12 14 11 9 8 9 16 10 0.7692 14 0.6923 0.6923
Randy Dick 11 7 8 8 9 14 10 10 13 11 10 9 14 10 0.7692 14 0.6923 0.6923
David Dupree 13 8 10 9 7 11 11 11 12 NA 9 8 13 10 0.7692 13 0.6804 0.6318
Bryson Scott 10 9 7 NA 7 12 11 12 10 9 NA 9 15 10 0.7692 12 0.6798 0.5827
Jeremy Stieler 11 9 6 11 6 13 11 11 11 9 9 8 16 10 0.7692 14 0.6779 0.6779
Philip Driskill 12 7 8 10 8 NA 13 11 10 NA NA 9 14 10 0.7692 11 0.6747 0.5301
Daniel Baller 14 6 9 8 7 9 10 12 10 10 10 10 15 10 0.7692 14 0.6731 0.6731
Brayant Rivera 10 8 9 8 6 13 11 10 12 9 11 10 13 10 0.7692 14 0.6731 0.6731
Robert Sokol 10 8 NA NA 6 9 9 13 12 9 10 8 14 10 0.7692 12 0.6705 0.5747
Erik Neumann 12 8 9 9 7 13 10 11 12 9 10 NA NA 10 0.7692 12 0.6704 0.5746
Kevin Buettner 12 8 8 10 7 11 10 9 10 10 10 8 16 10 0.7692 14 0.6683 0.6683
Rafael Torres 12 9 8 7 8 10 12 10 12 11 11 7 12 10 0.7692 14 0.6683 0.6683
Karen Coleman 13 6 NA 11 9 9 10 9 11 8 9 9 14 10 0.7692 13 0.6667 0.6191
Pablo Burgosramos 9 5 8 9 5 14 12 12 12 7 11 10 13 10 0.7692 14 0.6587 0.6587
Stephen Bush 9 7 4 10 9 13 13 9 10 10 8 9 16 10 0.7692 14 0.6587 0.6587
Ryan Cvik 10 8 9 11 9 11 11 13 10 7 7 9 12 10 0.7692 14 0.6587 0.6587
Kamar Morgan 12 6 8 5 8 12 9 12 10 8 10 10 16 10 0.7692 14 0.6538 0.6538
Scott Lefton 10 8 8 7 7 11 11 10 11 10 10 8 15 10 0.7692 14 0.6538 0.6538
Anthony Brinson 11 7 NA 9 10 11 9 12 6 NA NA 8 14 10 0.7692 11 0.6524 0.5126
Zechariah Ziebarth 8 8 8 10 5 10 10 11 11 NA 12 8 15 10 0.7692 13 0.6495 0.6031
Michael Moore 11 6 7 7 8 12 NA 9 9 NA 12 9 16 10 0.7692 12 0.6480 0.5554
Michael Moss 13 8 8 8 10 13 8 9 11 9 10 6 11 10 0.7692 14 0.6442 0.6442
Karen Richardson 10 9 7 9 11 8 8 12 8 10 9 9 13 10 0.7692 14 0.6394 0.6394
Cheryl Brown 11 6 9 8 8 10 NA 9 8 10 11 9 14 10 0.7692 13 0.6373 0.5918
Jonathon Leslein 10 8 7 10 8 12 10 10 8 10 7 10 12 10 0.7692 14 0.6346 0.6346
Ramar Williams 10 8 7 11 8 11 11 10 8 8 9 8 13 10 0.7692 14 0.6346 0.6346
Jason Jackson 12 7 5 6 5 12 9 11 10 10 12 9 NA 10 0.7692 13 0.6146 0.5707
Louie Renew 9 8 12 4 10 8 8 11 11 8 10 9 NA 10 0.7692 13 0.6146 0.5707
Anthony Rockemore 13 8 6 8 7 NA 8 NA NA 9 9 9 12 10 0.7692 11 0.6074 0.4772
Jose Torres Mendoza 12 8 8 8 NA NA 8 9 10 11 8 7 NA 10 0.7692 11 0.6037 0.4743
Richard Conkle 7 6 6 8 7 10 12 11 9 NA 8 NA NA 10 0.7692 11 0.5697 0.4476
Andrew Gray 5 8 9 7 NA NA 7 9 7 11 8 6 5 10 0.7692 12 0.5111 0.4381
Steven Maisonneuve NA NA NA NA 11 10 11 12 11 8 NA 10 12 9 0.6923 9 0.7231 0.4648
Bruce Williams 13 9 10 8 9 13 12 10 NA 10 10 9 14 9 0.6923 13 0.7047 0.6544
Marc Agne 14 7 9 13 6 13 10 9 12 10 10 7 16 9 0.6923 14 0.6971 0.6971
Pamela Augustine 14 9 9 NA 7 11 9 NA 10 NA 10 9 14 9 0.6923 11 0.6852 0.5384
Matthew Schultz 13 10 9 8 9 9 9 12 11 8 11 10 14 9 0.6923 14 0.6827 0.6827
Heather Ellenberger 13 8 7 8 7 12 11 11 13 9 9 10 14 9 0.6923 14 0.6779 0.6779
Anthony Bloss 13 8 8 11 8 13 11 11 9 7 10 9 14 9 0.6923 14 0.6779 0.6779
George Sweet 13 9 6 10 11 9 11 11 12 7 10 11 12 9 0.6923 14 0.6779 0.6779
Shawn Carden 10 9 10 10 8 11 10 11 11 9 8 8 15 9 0.6923 14 0.6683 0.6683
Darryle Sellers 11 11 6 8 9 11 9 10 12 9 12 7 15 9 0.6923 14 0.6683 0.6683
Daniel Halse 12 6 8 10 7 13 9 11 11 11 NA 8 14 9 0.6923 13 0.6649 0.6174
Kevin Kehoe 13 7 9 10 8 13 12 11 9 8 8 8 13 9 0.6923 14 0.6635 0.6635
Nicole Dike 13 7 8 10 7 10 10 12 10 9 10 8 15 9 0.6923 14 0.6635 0.6635
Walter Archambo 8 8 7 9 6 12 11 11 12 10 10 9 15 9 0.6923 14 0.6587 0.6587
Jason Schattel 13 7 6 9 10 11 9 10 11 9 10 10 13 9 0.6923 14 0.6587 0.6587
Nahir Shepard 11 8 10 8 6 12 8 12 9 9 11 10 13 9 0.6923 14 0.6538 0.6538
Michelle Fraterrigo 11 8 9 9 7 11 12 12 11 8 8 10 NA 9 0.6923 13 0.6510 0.6045
Jeffrey Rudderforth 11 11 10 9 6 7 10 11 12 9 8 8 14 9 0.6923 14 0.6490 0.6490
Jennifer Arty 10 7 9 7 7 12 8 12 11 9 10 8 15 9 0.6923 14 0.6442 0.6442
David Humes 10 9 8 11 5 8 12 8 12 11 11 6 14 9 0.6923 14 0.6442 0.6442
Thomas Mccoy 10 7 6 8 9 11 11 10 12 10 10 8 13 9 0.6923 14 0.6442 0.6442
Antonio Mitchell 11 7 8 9 9 11 10 11 10 8 8 9 13 9 0.6923 14 0.6394 0.6394
Michael Branson 9 8 8 9 8 11 9 11 10 9 11 7 14 9 0.6923 14 0.6394 0.6394
Brian Patterson 11 6 9 9 6 NA 9 13 NA 9 12 7 14 9 0.6923 12 0.6369 0.5459
Trevor Macgavin 12 7 10 8 8 8 9 7 10 7 11 10 16 9 0.6923 14 0.6346 0.6346
Amy Asberry 11 8 6 10 NA 12 9 NA 9 8 NA NA 13 9 0.6923 10 0.6291 0.4494
Joshua Tracey 12 5 8 6 7 NA 9 13 10 7 10 9 16 9 0.6923 13 0.6237 0.5792
Noah Gosswiller 8 7 NA 10 8 NA 10 11 10 10 10 8 NA 9 0.6923 11 0.6235 0.4899
Matthew Olguin 10 8 9 9 7 12 11 11 9 7 8 5 14 9 0.6923 14 0.6202 0.6202
Nicholas Nguyen 11 8 5 8 7 12 11 9 10 9 10 9 NA 9 0.6923 13 0.6146 0.5707
Jonathan Smith 11 NA 4 10 7 NA 8 11 10 7 9 7 15 9 0.6923 12 0.6067 0.5200
Sheryl Claiborne-Smith 11 7 NA NA NA 7 7 10 7 7 9 9 10 9 0.6923 11 0.5741 0.4511
Gabriel Quinones 10 7 6 9 NA 11 8 7 NA NA NA 6 NA 9 0.6923 9 0.5407 0.3476
Nicholas Cinco 12 8 NA NA 6 11 11 12 11 11 8 9 12 8 0.6154 12 0.6761 0.5795
Ryan Baum 14 4 9 10 9 NA 10 10 11 10 NA 8 14 8 0.6154 12 0.6500 0.5571
Clevante Granville 9 11 NA NA 5 11 11 9 10 11 9 10 NA 8 0.6154 11 0.6500 0.5107
Montee Brown 10 6 8 7 8 14 11 10 8 10 10 9 14 8 0.6154 14 0.6394 0.6394
Steward Hogans 10 7 10 NA NA NA NA 10 13 8 8 9 11 8 0.6154 10 0.6309 0.4506
James Small 12 NA 9 10 8 10 9 9 10 10 NA 6 11 8 0.6154 12 0.6292 0.5393
Brandon Parks 12 6 9 9 6 13 NA NA 12 10 10 6 NA 8 0.6154 11 0.6273 0.4929
Kristen White 14 7 9 9 8 9 9 8 8 9 9 8 13 8 0.6154 14 0.6154 0.6154
Marcus Evans 11 8 NA 8 7 10 7 9 10 6 11 8 12 8 0.6154 13 0.5990 0.5562
Ronald Schmidt 10 10 5 9 6 8 12 10 NA 7 9 10 NA 8 0.6154 12 0.5876 0.5037
Steven Webster 7 7 9 6 7 9 NA 11 NA 8 10 8 NA 8 0.6154 11 0.5556 0.4365
Robert Martin 7 NA 9 8 8 8 7 NA 8 7 7 9 11 8 0.6154 12 0.5511 0.4724
Chris Papageorge 14 8 10 11 8 12 12 12 11 8 9 7 15 7 0.5385 14 0.6923 0.6923
Paul Seitz 11 9 9 NA 8 10 11 NA NA 7 8 8 14 7 0.5385 11 0.6335 0.4977
Melissa Printup 8 9 9 6 10 10 10 10 7 10 9 8 13 7 0.5385 14 0.6058 0.6058
Derrick Elam 13 9 8 11 7 10 8 9 NA NA NA 7 NA 7 0.5385 10 0.5973 0.4266
Kyle May 10 8 5 6 8 NA 12 10 9 8 8 8 13 7 0.5385 13 0.5773 0.5361
Megan Fitzgerald 8 11 9 10 NA NA 8 10 NA NA NA 7 13 4 0.3077 9 0.5839 0.3754
Clayton Grimes 14 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.8750 0.0625
Tanaysa Henderson NA NA NA NA NA 12 NA NA NA NA NA NA NA NA 0.0000 1 0.8571 0.0612
Wallace Savage 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.7500 0.0536
Brian Holder 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.7500 0.0536
Sandra Carter 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.7500 0.0536
Ryan Wiggins NA NA NA NA NA NA NA NA 11 NA NA NA NA NA 0.0000 1 0.7333 0.0524
Heather Kohler 12 NA 7 12 9 11 NA 12 NA 8 9 10 14 NA 0.0000 10 0.6980 0.4986
Michael Linder 11 9 9 NA NA 12 10 11 10 NA 11 NA 13 NA 0.0000 9 0.6957 0.4472
Jeremy Krammes 12 NA NA NA NA NA NA 10 NA 10 NA NA NA NA 0.0000 3 0.6957 0.1491
Terrence Lee 11 NA NA NA NA NA NA NA NA NA NA 9 NA NA 0.0000 2 0.6897 0.0985
Daniel Gray 11 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.6875 0.0491
Jeremy Mounce 12 8 8 NA 10 12 NA NA NA 10 10 NA NA NA 0.0000 7 0.6731 0.3366
Travis Delagardelle 11 12 10 8 6 11 12 11 11 9 9 NA NA NA 0.0000 11 0.6627 0.5207
George Hall 12 NA 8 NA NA NA NA NA NA 10 NA NA NA NA 0.0000 3 0.6522 0.1398
David Plate 10 8 8 8 9 NA NA NA 13 10 8 9 14 NA 0.0000 10 0.6467 0.4619
Paul Presti 12 8 9 12 7 11 8 10 NA 10 9 NA NA NA 0.0000 10 0.6358 0.4541
Tara Bridgett 11 8 8 8 NA 9 NA 10 10 NA NA NA 15 NA 0.0000 8 0.6320 0.3611
Keisha Vasquez 8 7 9 9 11 11 9 12 8 8 NA NA 14 NA 0.0000 11 0.6310 0.4958
Jordan Forwood 11 8 6 11 NA 13 NA 10 NA NA NA NA NA NA 0.0000 6 0.6277 0.2690
Kenneth Nielsen 13 8 7 NA 8 9 11 10 NA NA NA NA 11 NA 0.0000 8 0.6260 0.3577
Diance Durand 9 9 12 7 8 10 9 11 11 7 9 8 12 NA 0.0000 13 0.6256 0.5809
Desmond Jenkins 10 7 7 NA 7 12 8 NA NA NA NA NA 15 NA 0.0000 7 0.6168 0.3084
Yiming Hu 12 NA 7 7 6 8 12 9 NA 9 12 8 11 NA 0.0000 11 0.6159 0.4839
Vincent Scannelli 11 7 7 11 8 8 11 12 9 8 9 7 12 NA 0.0000 13 0.6154 0.5714
David Hadley 13 10 8 NA 8 NA 8 NA NA NA NA NA NA NA 0.0000 5 0.6104 0.2180
Terry Hardison 13 8 6 7 4 11 10 12 11 9 11 7 NA NA 0.0000 12 0.6089 0.5219
Bunnaro Sun 12 5 8 11 6 8 9 9 12 8 8 NA 14 NA 0.0000 12 0.6044 0.5181
Wayne Gokey 13 7 NA 11 NA NA 8 NA 8 NA NA NA NA NA 0.0000 5 0.6026 0.2152
Jonathan Knight 13 10 9 6 7 NA 11 NA NA NA NA NA NA NA 0.0000 6 0.6022 0.2581
Kevin Green 11 9 NA 8 7 12 NA 8 NA NA NA NA NA NA 0.0000 6 0.5978 0.2562
Ryan Shipley 11 6 10 8 5 9 11 NA 10 NA 9 10 NA NA 0.0000 10 0.5973 0.4266
Jeffrey Dusza 11 8 NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 2 0.5938 0.0848
Cherylynn Vidal 13 9 8 8 NA NA NA NA NA NA NA NA NA NA 0.0000 4 0.5938 0.1697
Adam Konkle 10 9 NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 2 0.5938 0.0848
Thomas Cho 10 6 NA 11 7 12 NA 8 9 NA NA NA NA NA 0.0000 7 0.5888 0.2944
Jason Miranda 10 7 8 NA 9 11 8 NA NA NA NA NA NA NA 0.0000 6 0.5824 0.2496
Jennifer Wilson 11 9 10 6 NA 7 11 NA NA NA NA NA NA NA 0.0000 6 0.5806 0.2488
Akilah Gamble 9 NA 12 9 6 8 12 6 NA 8 NA 7 NA NA 0.0000 9 0.5746 0.3694
Robert Lynch 6 9 8 6 9 7 7 12 NA 9 7 8 15 NA 0.0000 12 0.5722 0.4905
Joseph Martin 10 7 8 8 8 10 9 NA NA NA NA NA NA NA 0.0000 7 0.5607 0.2804
Min Choi 10 NA 7 NA 8 7 NA 10 NA NA NA NA NA NA 0.0000 5 0.5526 0.1974
Lawrence Thuotte 9 5 12 NA 8 NA NA NA NA NA NA NA NA NA 0.0000 4 0.5484 0.1567
Donald Park 9 NA 6 NA NA 10 NA NA NA NA NA NA NA NA 0.0000 3 0.5435 0.1165
Monte Henderson 9 8 NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 2 0.5312 0.0759
David Kim 9 8 NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 2 0.5312 0.0759
Jamie Ainsleigh-Wong 9 8 9 9 8 5 NA NA NA NA NA NA NA NA 0.0000 6 0.5217 0.2236
Jay Kelly 10 9 7 7 5 10 7 NA NA NA NA NA NA NA 0.0000 7 0.5140 0.2570
Zachary Brosemer 8 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.5000 0.0357
Antonio Chapa 8 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.5000 0.0357
Vincent Kandian 9 8 8 7 NA NA NA NA NA NA NA NA NA NA 0.0000 4 0.5000 0.1429
Ashley Johnson 9 NA 6 NA 6 NA NA NA NA NA 5 9 8 NA 0.0000 6 0.4831 0.2070
Ashlyn Dortch 9 NA NA 8 NA 5 9 6 NA NA NA NA NA NA 0.0000 5 0.4805 0.1716
Gabrieal Feiling 10 NA 5 NA NA NA NA NA NA NA NA NA NA NA 0.0000 2 0.4688 0.0670
Jasprin Smith 6 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.3750 0.0268
Robert Epps NA 6 NA NA NA NA NA NA NA NA NA NA NA NA 0.0000 1 0.3750 0.0268

Season Leaderboard

Season Leaderboard (Season Percent)
Week 14
Season Rank Name Donuts Won Weeks Picked Season Percent Adj Season Percent Season Trend
1 Clayton Grimes 0 1 0.8750 0.0625
2 Tanaysa Henderson 0 1 0.8571 0.0612
3 Brittany Pillar 1 7 0.8081 0.4040
4 Brian Holder 0 1 0.7500 0.0536
4 Sandra Carter 0 1 0.7500 0.0536
4 Wallace Savage 0 1 0.7500 0.0536
7 Matthew Blair 0 9 0.7462 0.4797
8 Ryan Wiggins 0 1 0.7333 0.0524
9 Robert Cunningham 0 13 0.7240 0.6723
10 Steven Maisonneuve 1 9 0.7231 0.4648
11 Randolph Tidd 1 13 0.7165 0.6653
12 Aubrey Conn 2 14 0.7163 0.7163
12 Robert Gelo 1 14 0.7163 0.7163
14 Michael Pacifico 1 14 0.7115 0.7115
15 Nathan Brown 1 13 0.7113 0.6605
16 Bruce Williams 0 13 0.7047 0.6544
17 Heather Kohler 0 10 0.6980 0.4986
18 Chester Todd 1 14 0.6971 0.6971
18 Marc Agne 2 14 0.6971 0.6971
20 Bradley Hobson 1 13 0.6959 0.6462
21 Jeremy Krammes 0 3 0.6957 0.1491
21 Michael Linder 0 9 0.6957 0.4472
23 Brian Hollmann 1 11 0.6938 0.5451
24 Chris Papageorge 0 14 0.6923 0.6923
24 Christopher Sims 2 14 0.6923 0.6923
24 Keven Talbert 2 14 0.6923 0.6923
24 Randy Dick 1 14 0.6923 0.6923
28 Terrence Lee 0 2 0.6897 0.0985
29 Patrick Tynan 1 13 0.6891 0.6399
30 Daniel Gray 0 1 0.6875 0.0491
30 Gregory Brown 2 14 0.6875 0.6875
32 Pamela Augustine 0 11 0.6852 0.5384
33 William Schouviller 1 13 0.6839 0.6350
34 Matthew Schultz 0 14 0.6827 0.6827
35 David Dupree 0 13 0.6804 0.6318
36 Bryson Scott 0 12 0.6798 0.5827
37 Anthony Bloss 0 14 0.6779 0.6779
37 George Sweet 2 14 0.6779 0.6779
37 Heather Ellenberger 0 14 0.6779 0.6779
37 Jeremy Stieler 1 14 0.6779 0.6779
37 Shaun Dahl 1 14 0.6779 0.6779
42 Thomas Brenstuhl 1 13 0.6771 0.6287
43 Nicholas Cinco 0 12 0.6761 0.5795
44 Philip Driskill 1 11 0.6747 0.5301
45 Brayant Rivera 0 14 0.6731 0.6731
45 Daniel Baller 0 14 0.6731 0.6731
45 Jeremy Mounce 0 7 0.6731 0.3366
48 Jennifer Bouland 0 13 0.6719 0.6239
49 Robert Sokol 0 12 0.6705 0.5747
50 Erik Neumann 0 12 0.6704 0.5746
51 Daniel Major 0 13 0.6684 0.6207
52 Darryle Sellers 0 14 0.6683 0.6683
52 Kevin Buettner 1 14 0.6683 0.6683
52 Rafael Torres 0 14 0.6683 0.6683
52 Shawn Carden 0 14 0.6683 0.6683
56 Karen Coleman 0 13 0.6667 0.6191
57 Daniel Halse 0 13 0.6649 0.6174
58 George Brown 1 14 0.6635 0.6635
58 Kevin Kehoe 0 14 0.6635 0.6635
58 Nicole Dike 0 14 0.6635 0.6635
61 Travis Delagardelle 1 11 0.6627 0.5207
62 Earl Dixon 0 13 0.6598 0.6127
63 George Mancini 0 12 0.6591 0.5649
64 Edward Ford 0 14 0.6587 0.6587
64 Jason Schattel 0 14 0.6587 0.6587
64 Pablo Burgosramos 1 14 0.6587 0.6587
64 Richard Beeghley 1 14 0.6587 0.6587
64 Ryan Cvik 0 14 0.6587 0.6587
64 Stephen Bush 2 14 0.6587 0.6587
64 Walter Archambo 0 14 0.6587 0.6587
71 Kamar Morgan 1 14 0.6538 0.6538
71 Nahir Shepard 0 14 0.6538 0.6538
71 Scott Lefton 0 14 0.6538 0.6538
74 Anthony Brinson 0 11 0.6524 0.5126
75 George Hall 0 3 0.6522 0.1398
76 Michelle Fraterrigo 0 13 0.6510 0.6045
77 Clevante Granville 0 11 0.6500 0.5107
77 Ryan Baum 0 12 0.6500 0.5571
79 Zechariah Ziebarth 0 13 0.6495 0.6031
80 Jeffrey Rudderforth 0 14 0.6490 0.6490
81 Michael Moore 1 12 0.6480 0.5554
82 David Plate 0 10 0.6467 0.4619
83 David Humes 0 14 0.6442 0.6442
83 Jennifer Arty 0 14 0.6442 0.6442
83 Michael Moss 0 14 0.6442 0.6442
83 Thomas Mccoy 0 14 0.6442 0.6442
87 Jared Kaanga 0 13 0.6406 0.5948
88 Antonio Mitchell 0 14 0.6394 0.6394
88 Cade Martinez 0 14 0.6394 0.6394
88 Karen Richardson 1 14 0.6394 0.6394
88 Michael Branson 0 14 0.6394 0.6394
88 Montee Brown 1 14 0.6394 0.6394
93 Jeffrey Zornes 0 13 0.6392 0.5935
94 Cheryl Brown 0 13 0.6373 0.5918
95 Brian Patterson 0 12 0.6369 0.5459
96 Paul Presti 0 10 0.6358 0.4541
97 Jonathon Leslein 0 14 0.6346 0.6346
97 Ramar Williams 0 14 0.6346 0.6346
97 Trevor Macgavin 1 14 0.6346 0.6346
100 Christopher Mulcahy 0 13 0.6340 0.5887
101 Paul Seitz 0 11 0.6335 0.4977
102 Tara Bridgett 0 8 0.6320 0.3611
103 Keisha Vasquez 1 11 0.6310 0.4958
104 Steward Hogans 0 10 0.6309 0.4506
105 Darvin Graham 0 12 0.6307 0.5406
106 Rachel Follo 1 14 0.6298 0.6298
107 James Small 0 12 0.6292 0.5393
108 Amy Asberry 0 10 0.6291 0.4494
109 Jordan Forwood 0 6 0.6277 0.2690
110 Brandon Parks 0 11 0.6273 0.4929
111 Kenneth Nielsen 0 8 0.6260 0.3577
112 Diance Durand 1 13 0.6256 0.5809
113 Joshua Tracey 1 13 0.6237 0.5792
114 Noah Gosswiller 0 11 0.6235 0.4899
115 Matthew Olguin 0 14 0.6202 0.6202
116 Desmond Jenkins 0 7 0.6168 0.3084
117 Yiming Hu 0 11 0.6159 0.4839
118 Kristen White 0 14 0.6154 0.6154
118 Vincent Scannelli 0 13 0.6154 0.5714
120 Jason Jackson 0 13 0.6146 0.5707
120 Louie Renew 1 13 0.6146 0.5707
120 Nicholas Nguyen 0 13 0.6146 0.5707
123 Wayne Schofield 1 14 0.6106 0.6106
124 David Hadley 0 5 0.6104 0.2180
125 Terry Hardison 0 12 0.6089 0.5219
126 Anthony Rockemore 0 11 0.6074 0.4772
127 Jonathan Smith 0 12 0.6067 0.5200
128 Melissa Printup 0 14 0.6058 0.6058
129 Bunnaro Sun 0 12 0.6044 0.5181
130 Jose Torres Mendoza 0 11 0.6037 0.4743
131 Jack Wheeler 0 13 0.6031 0.5600
132 Wayne Gokey 0 5 0.6026 0.2152
133 Jonathan Knight 0 6 0.6022 0.2581
134 Gary Lawrence 1 14 0.6010 0.6010
135 Marcus Evans 0 13 0.5990 0.5562
136 Kevin Green 0 6 0.5978 0.2562
137 Derrick Elam 0 10 0.5973 0.4266
137 Ryan Shipley 0 10 0.5973 0.4266
139 Adam Konkle 0 2 0.5938 0.0848
139 Cherylynn Vidal 0 4 0.5938 0.1697
139 Jeffrey Dusza 0 2 0.5938 0.0848
142 Thomas Cho 0 7 0.5888 0.2944
143 Ronald Schmidt 0 12 0.5876 0.5037
144 Megan Fitzgerald 0 9 0.5839 0.3754
145 Jason Miranda 0 6 0.5824 0.2496
146 Jennifer Wilson 0 6 0.5806 0.2488
147 Kyle May 0 13 0.5773 0.5361
148 Akilah Gamble 1 9 0.5746 0.3694
149 Sheryl Claiborne-Smith 0 11 0.5741 0.4511
150 Robert Lynch 0 12 0.5722 0.4905
151 Richard Conkle 0 11 0.5697 0.4476
152 Joseph Martin 0 7 0.5607 0.2804
153 Steven Webster 0 11 0.5556 0.4365
154 Min Choi 0 5 0.5526 0.1974
155 Robert Martin 0 12 0.5511 0.4724
156 Lawrence Thuotte 1 4 0.5484 0.1567
157 Donald Park 0 3 0.5435 0.1165
158 Gabriel Quinones 0 9 0.5407 0.3476
159 David Kim 0 2 0.5312 0.0759
159 Monte Henderson 0 2 0.5312 0.0759
161 Jamie Ainsleigh-Wong 0 6 0.5217 0.2236
162 Jay Kelly 0 7 0.5140 0.2570
163 Andrew Gray 0 12 0.5111 0.4381
164 Antonio Chapa 0 1 0.5000 0.0357
164 Vincent Kandian 0 4 0.5000 0.1429
164 Zachary Brosemer 0 1 0.5000 0.0357
167 Ashley Johnson 0 6 0.4831 0.2070
168 Ashlyn Dortch 0 5 0.4805 0.1716
169 Gabrieal Feiling 0 2 0.4688 0.0670
170 Jasprin Smith 0 1 0.3750 0.0268
170 Robert Epps 0 1 0.3750 0.0268

Adjusted Season Leaderboard

Season Leaderboard (Adjusted Season Percent)
Week 14
Season Rank Name Donuts Won Weeks Picked Season Percent Adj Season Percent Season Trend
1 Aubrey Conn 2 14 0.7163 0.7163
1 Robert Gelo 1 14 0.7163 0.7163
3 Michael Pacifico 1 14 0.7115 0.7115
4 Chester Todd 1 14 0.6971 0.6971
4 Marc Agne 2 14 0.6971 0.6971
6 Chris Papageorge 0 14 0.6923 0.6923
6 Christopher Sims 2 14 0.6923 0.6923
6 Keven Talbert 2 14 0.6923 0.6923
6 Randy Dick 1 14 0.6923 0.6923
10 Gregory Brown 2 14 0.6875 0.6875
11 Matthew Schultz 0 14 0.6827 0.6827
12 Anthony Bloss 0 14 0.6779 0.6779
12 George Sweet 2 14 0.6779 0.6779
12 Heather Ellenberger 0 14 0.6779 0.6779
12 Jeremy Stieler 1 14 0.6779 0.6779
12 Shaun Dahl 1 14 0.6779 0.6779
17 Brayant Rivera 0 14 0.6731 0.6731
17 Daniel Baller 0 14 0.6731 0.6731
19 Robert Cunningham 0 13 0.7240 0.6723
20 Darryle Sellers 0 14 0.6683 0.6683
20 Kevin Buettner 1 14 0.6683 0.6683
20 Rafael Torres 0 14 0.6683 0.6683
20 Shawn Carden 0 14 0.6683 0.6683
24 Randolph Tidd 1 13 0.7165 0.6653
25 George Brown 1 14 0.6635 0.6635
25 Kevin Kehoe 0 14 0.6635 0.6635
25 Nicole Dike 0 14 0.6635 0.6635
28 Nathan Brown 1 13 0.7113 0.6605
29 Edward Ford 0 14 0.6587 0.6587
29 Jason Schattel 0 14 0.6587 0.6587
29 Pablo Burgosramos 1 14 0.6587 0.6587
29 Richard Beeghley 1 14 0.6587 0.6587
29 Ryan Cvik 0 14 0.6587 0.6587
29 Stephen Bush 2 14 0.6587 0.6587
29 Walter Archambo 0 14 0.6587 0.6587
36 Bruce Williams 0 13 0.7047 0.6544
37 Kamar Morgan 1 14 0.6538 0.6538
37 Nahir Shepard 0 14 0.6538 0.6538
37 Scott Lefton 0 14 0.6538 0.6538
40 Jeffrey Rudderforth 0 14 0.6490 0.6490
41 Bradley Hobson 1 13 0.6959 0.6462
42 David Humes 0 14 0.6442 0.6442
42 Jennifer Arty 0 14 0.6442 0.6442
42 Michael Moss 0 14 0.6442 0.6442
42 Thomas Mccoy 0 14 0.6442 0.6442
46 Patrick Tynan 1 13 0.6891 0.6399
47 Antonio Mitchell 0 14 0.6394 0.6394
47 Cade Martinez 0 14 0.6394 0.6394
47 Karen Richardson 1 14 0.6394 0.6394
47 Michael Branson 0 14 0.6394 0.6394
47 Montee Brown 1 14 0.6394 0.6394
52 William Schouviller 1 13 0.6839 0.6350
53 Jonathon Leslein 0 14 0.6346 0.6346
53 Ramar Williams 0 14 0.6346 0.6346
53 Trevor Macgavin 1 14 0.6346 0.6346
56 David Dupree 0 13 0.6804 0.6318
57 Rachel Follo 1 14 0.6298 0.6298
58 Thomas Brenstuhl 1 13 0.6771 0.6287
59 Jennifer Bouland 0 13 0.6719 0.6239
60 Daniel Major 0 13 0.6684 0.6207
61 Matthew Olguin 0 14 0.6202 0.6202
62 Karen Coleman 0 13 0.6667 0.6191
63 Daniel Halse 0 13 0.6649 0.6174
64 Kristen White 0 14 0.6154 0.6154
65 Earl Dixon 0 13 0.6598 0.6127
66 Wayne Schofield 1 14 0.6106 0.6106
67 Melissa Printup 0 14 0.6058 0.6058
68 Michelle Fraterrigo 0 13 0.6510 0.6045
69 Zechariah Ziebarth 0 13 0.6495 0.6031
70 Gary Lawrence 1 14 0.6010 0.6010
71 Jared Kaanga 0 13 0.6406 0.5948
72 Jeffrey Zornes 0 13 0.6392 0.5935
73 Cheryl Brown 0 13 0.6373 0.5918
74 Christopher Mulcahy 0 13 0.6340 0.5887
75 Bryson Scott 0 12 0.6798 0.5827
76 Diance Durand 1 13 0.6256 0.5809
77 Nicholas Cinco 0 12 0.6761 0.5795
78 Joshua Tracey 1 13 0.6237 0.5792
79 Robert Sokol 0 12 0.6705 0.5747
80 Erik Neumann 0 12 0.6704 0.5746
81 Vincent Scannelli 0 13 0.6154 0.5714
82 Jason Jackson 0 13 0.6146 0.5707
82 Louie Renew 1 13 0.6146 0.5707
82 Nicholas Nguyen 0 13 0.6146 0.5707
85 George Mancini 0 12 0.6591 0.5649
86 Jack Wheeler 0 13 0.6031 0.5600
87 Ryan Baum 0 12 0.6500 0.5571
88 Marcus Evans 0 13 0.5990 0.5562
89 Michael Moore 1 12 0.6480 0.5554
90 Brian Patterson 0 12 0.6369 0.5459
91 Brian Hollmann 1 11 0.6938 0.5451
92 Darvin Graham 0 12 0.6307 0.5406
93 James Small 0 12 0.6292 0.5393
94 Pamela Augustine 0 11 0.6852 0.5384
95 Kyle May 0 13 0.5773 0.5361
96 Philip Driskill 1 11 0.6747 0.5301
97 Terry Hardison 0 12 0.6089 0.5219
98 Travis Delagardelle 1 11 0.6627 0.5207
99 Jonathan Smith 0 12 0.6067 0.5200
100 Bunnaro Sun 0 12 0.6044 0.5181
101 Anthony Brinson 0 11 0.6524 0.5126
102 Clevante Granville 0 11 0.6500 0.5107
103 Ronald Schmidt 0 12 0.5876 0.5037
104 Heather Kohler 0 10 0.6980 0.4986
105 Paul Seitz 0 11 0.6335 0.4977
106 Keisha Vasquez 1 11 0.6310 0.4958
107 Brandon Parks 0 11 0.6273 0.4929
108 Robert Lynch 0 12 0.5722 0.4905
109 Noah Gosswiller 0 11 0.6235 0.4899
110 Yiming Hu 0 11 0.6159 0.4839
111 Matthew Blair 0 9 0.7462 0.4797
112 Anthony Rockemore 0 11 0.6074 0.4772
113 Jose Torres Mendoza 0 11 0.6037 0.4743
114 Robert Martin 0 12 0.5511 0.4724
115 Steven Maisonneuve 1 9 0.7231 0.4648
116 David Plate 0 10 0.6467 0.4619
117 Paul Presti 0 10 0.6358 0.4541
118 Sheryl Claiborne-Smith 0 11 0.5741 0.4511
119 Steward Hogans 0 10 0.6309 0.4506
120 Amy Asberry 0 10 0.6291 0.4494
121 Richard Conkle 0 11 0.5697 0.4476
122 Michael Linder 0 9 0.6957 0.4472
123 Andrew Gray 0 12 0.5111 0.4381
124 Steven Webster 0 11 0.5556 0.4365
125 Derrick Elam 0 10 0.5973 0.4266
125 Ryan Shipley 0 10 0.5973 0.4266
127 Brittany Pillar 1 7 0.8081 0.4040
128 Megan Fitzgerald 0 9 0.5839 0.3754
129 Akilah Gamble 1 9 0.5746 0.3694
130 Tara Bridgett 0 8 0.6320 0.3611
131 Kenneth Nielsen 0 8 0.6260 0.3577
132 Gabriel Quinones 0 9 0.5407 0.3476
133 Jeremy Mounce 0 7 0.6731 0.3366
134 Desmond Jenkins 0 7 0.6168 0.3084
135 Thomas Cho 0 7 0.5888 0.2944
136 Joseph Martin 0 7 0.5607 0.2804
137 Jordan Forwood 0 6 0.6277 0.2690
138 Jonathan Knight 0 6 0.6022 0.2581
139 Jay Kelly 0 7 0.5140 0.2570
140 Kevin Green 0 6 0.5978 0.2562
141 Jason Miranda 0 6 0.5824 0.2496
142 Jennifer Wilson 0 6 0.5806 0.2488
143 Jamie Ainsleigh-Wong 0 6 0.5217 0.2236
144 David Hadley 0 5 0.6104 0.2180
145 Wayne Gokey 0 5 0.6026 0.2152
146 Ashley Johnson 0 6 0.4831 0.2070
147 Min Choi 0 5 0.5526 0.1974
148 Ashlyn Dortch 0 5 0.4805 0.1716
149 Cherylynn Vidal 0 4 0.5938 0.1697
150 Lawrence Thuotte 1 4 0.5484 0.1567
151 Jeremy Krammes 0 3 0.6957 0.1491
152 Vincent Kandian 0 4 0.5000 0.1429
153 George Hall 0 3 0.6522 0.1398
154 Donald Park 0 3 0.5435 0.1165
155 Terrence Lee 0 2 0.6897 0.0985
156 Adam Konkle 0 2 0.5938 0.0848
156 Jeffrey Dusza 0 2 0.5938 0.0848
158 David Kim 0 2 0.5312 0.0759
158 Monte Henderson 0 2 0.5312 0.0759
160 Gabrieal Feiling 0 2 0.4688 0.0670
161 Clayton Grimes 0 1 0.8750 0.0625
162 Tanaysa Henderson 0 1 0.8571 0.0612
163 Brian Holder 0 1 0.7500 0.0536
163 Sandra Carter 0 1 0.7500 0.0536
163 Wallace Savage 0 1 0.7500 0.0536
166 Ryan Wiggins 0 1 0.7333 0.0524
167 Daniel Gray 0 1 0.6875 0.0491
168 Antonio Chapa 0 1 0.5000 0.0357
168 Zachary Brosemer 0 1 0.5000 0.0357
170 Jasprin Smith 0 1 0.3750 0.0268
170 Robert Epps 0 1 0.3750 0.0268

Data

---
title: "2024 NFL Moneyline Picks"
output: 
  flexdashboard::flex_dashboard:
    theme:
      version: 4
      bootswatch: spacelab
    orientation: rows
    vertical_layout: fill
    social: ["menu"]
    source_code: embed
    navbar:
      - { title: "Created by: Daniel Baller", icon: "fa-github", href: "https://github.com/danielpballer"  }
---


```{r setup, include=FALSE}
#    source_code: embed
library(flexdashboard)
library(tidyverse)
library(data.table)
library(formattable)
library(ggpubr)
library(ggrepel)
library(gt)
library(glue)
library(ggthemes)
library(hrbrthemes)
library(sparkline)
library(plotly)
library(htmlwidgets)
library(mdthemes)
library(ggtext)
library(ggnewscale)
library(DT)
source("./Functions/functions2.R")

thematic::thematic_rmd(font = "auto")

```

```{r Reading in our picks files, include=FALSE}
current_week = 14 #Set what week it is
week_1 = read_csv("./CSV_Data_Files/2024 NFL Week 1.csv") %>% 
  mutate(Name = str_to_title(Name))
week_2 = read_csv("./CSV_Data_Files/2024 NFL Week 2.csv")%>% 
  mutate(Name = str_to_title(Name))
week_3 = read_csv("./CSV_Data_Files/2024 NFL Week 3.csv")%>% 
  mutate(Name = str_to_title(Name))
week_4 = read_csv("./CSV_Data_Files/2024 NFL Week 4.csv")%>%
 mutate(Name = str_to_title(Name))
week_5 = read_csv("./CSV_Data_Files/2024 NFL Week 5.csv")%>% 
  mutate(Name = str_to_title(Name))
week_6 = read_csv("./CSV_Data_Files/2024 NFL Week 6.csv")%>% 
  mutate(Name = str_to_title(Name))
week_7 = read_csv("./CSV_Data_Files/2024 NFL Week 7.csv")%>% 
  mutate(Name = str_to_title(Name))
week_8 = read_csv("./CSV_Data_Files/2024 NFL Week 8.csv")%>% 
  mutate(Name = str_to_title(Name))
 week_9 = read_csv("./CSV_Data_Files/2024 NFL Week 9.csv")%>% 
  mutate(Name = str_to_title(Name))
week_10 = read_csv("./CSV_Data_Files/2024 NFL Week 10.csv")%>% 
  mutate(Name = str_to_title(Name))
week_11 = read_csv("./CSV_Data_Files/2024 NFL Week 11.csv")%>% 
  mutate(Name = str_to_title(Name))
week_12 = read_csv("./CSV_Data_Files/2024 NFL Week 12.csv")%>% 
  mutate(Name = str_to_title(Name))
week_13 = read_csv("./CSV_Data_Files/2024 NFL Week 13.csv")%>% 
  mutate(Name = str_to_title(Name))
week_14 = read_csv("./CSV_Data_Files/2024 NFL Week 14.csv")%>% 
  mutate(Name = str_to_title(Name))
# week_15 = read_csv("./CSV_Data_Files/2024 NFL Week 15.csv")%>% 
#  mutate(Name = str_to_title(Name))
# week_16 = read_csv("./CSV_Data_Files/2024 NFL Week 16.csv")%>% 
#  mutate(Name = str_to_title(Name))
# week_17 = read_csv("./CSV_Data_Files/2024 NFL Week 17.csv")%>% 
#  mutate(Name = str_to_title(Name))
# week_18 = read_csv("./CSV_Data_Files/2024 NFL Week 18.csv")%>% 
#  mutate(Name = str_to_title(Name))
# week_19 = read_csv("./CSV_Data_Files/2024 NFL Wild Card.csv")%>% 
#  mutate(Name = str_to_title(Name))
# week_20 = read_csv("./CSV_Data_Files/2024 NFL Divisional Round.csv")%>% 
#  mutate(Name = str_to_title(Name))
# week_21 = read_csv("./CSV_Data_Files/2024 NFL Conference Round.csv")%>% 
#  mutate(Name = str_to_title(Name))
# week_22 = read_csv("./CSV_Data_Files/2024 NFL Super Bowl.csv")%>% 
#  mutate(Name = str_to_title(Name))

#reading in scores
Scores = read_csv(glue::glue("./CSV_Data_Files/NFL_Scores_{current_week}.csv")) 

#reading in CBS Prediction Records
cbs = read_csv(glue::glue("./CSV_Data_Files/CBS_Experts_{current_week}.csv")) %>% 
  mutate(Percent = round(Percent,4))
cbs_season = read_csv(glue::glue("./CSV_Data_Files/CBS_Experts_Season_{current_week}.csv"))

#reading in ESPN Prediction Records
espn = read_csv(glue::glue("./CSV_Data_Files/ESPN_Experts_{current_week}.csv"))%>% 
  mutate(Percent = round(Percent,4))
espn_season = read_csv(glue::glue("./CSV_Data_Files/ESPN_Experts_Season_{current_week}.csv"))%>% 
  mutate(Percent = round(Percent,4))

#Odds not working for the 2024 season.  Need to fix scrape code for next year.
#Reading in the moneyline odds for each team and cleaning the team names
# odds_wk1 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_1.csv"))
# odds_wk2 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_2.csv"))
# odds_wk3 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_3.csv"))
# odds_wk4 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_4.csv"))
# odds_wk5 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_5.csv"))
# odds_wk6 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_6.csv"))
# odds_wk7 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_7.csv"))
# odds_wk8 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_8.csv"))
# odds_wk9 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_9.csv"))
# odds_wk10 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_10.csv"))
# odds_wk11 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_11.csv"))
# odds_wk12 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_12.csv"))
# odds_wk13 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_13.csv"))
# odds_wk14 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_14.csv"))
# odds_wk15 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_15.csv"))
# odds_wk16 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_16.csv"))
# odds_wk17 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_17.csv"))
# odds_wk18 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_18.csv"))
# odds_wk19 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_19.csv"))
# odds_wk20 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_20.csv"))
# odds_wk21 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_21.csv"))
# odds_wk22 = read_csv(glue::glue("./CSV_Data_Files/Moneyline_Odds_22.csv"))

####################UPDATE THESE###############################
inst.picks = list(week_1, week_2, week_3, week_4, week_5, week_6, week_7, week_8 , week_9, week_10, week_11, week_12, week_13, week_14) #, week_15, week_16, week_17 , week_18, week_19 , week_20, week_21, week_22) #add in the additional weeks
# odds = rbind(odds_wk1, odds_wk2, odds_wk3, odds_wk4, odds_wk5, odds_wk6, odds_wk7, odds_wk8,
#              odds_wk9, odds_wk10, odds_wk11, odds_wk12) #add in the additional weeks
####################END OF UPDATE##############################

weeks = as.list(seq(1:current_week)) #creating a list of each week number
```

```{r read in scores clean data, include=FALSE}
#Cleaning Odds Data
# cl_odds = odds_cleaning(odds)

#Cleaning scores data
Scores = cleaning2(Scores)

#creating a list of winners for each week
winners = map(weeks, weekly_winners)

#creating a vector of this weeks winners
this_week = pull(winners[[length(winners)]])  

#Getting the number of games for each week
weekly_number_of_games = map_dbl(weeks, week_number_games)
```

```{r Group Predictions, include=FALSE}
#Creating the list of everyones predictions each week.
games = map(inst.picks, games_fn)

#Creating the prediction table.  
pred_table = map(games, pred_table_fn)

#Adding who won to the predictions
with_winners = map2(pred_table, winners, adding_winners)

#Creating results for each week.
results = map2(with_winners,weekly_number_of_games, results_fn)
```


```{r Displaying Group Results, echo=FALSE}
#Displaying the group results

inst_group_table = results[[length(results)]] %>% gt() %>% 
  cols_align(
    align = "center") %>% 
   tab_header(
    title = md("This Week's Predictions"),
    #subtitle = md(glue("Week {length(results)}"))
    ) %>% 
   tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(Correct),
      rows = Correct =="No"
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(Correct),
      rows = Correct =="Yes"
    )) %>% 
  tab_options(
    data_row.padding = px(3),
    container.height = "100%"
   )
```

```{r Weekly and season Group Results, include=FALSE}
# Printing the weekly and season win percentage     

#how many games correct, incorrect, and not picked each week
weekly_group_correct = map(results, weekly_group_correct_fn)  

#how many games were picked each week
weekly_games_picked = map2(weekly_group_correct, weekly_number_of_games, weekly_games_picked_fn)

#Calculating the number of correct picks for each week
weekly_group_correct_picks = map(weekly_group_correct, weekly_group_correct_picks_fn)

# Code to manually hard code in week where we get 0 games correct
# ##### Remove this line before next season 
# weekly_group_correct_picks[[21]]=0

#Calculating weekly win percentage
weekly_win_percentage = map2(weekly_group_correct_picks, weekly_games_picked, weekly_win_percentage_fn)

#Calculating season win percentage
season_win_percentage = round(sum(unlist(weekly_group_correct_picks))/sum(unlist(weekly_games_picked)),4)

#Calculating number of games picked this season
season_games = sum(unlist(weekly_games_picked))

#calculating season wins
season_wins = sum(unlist(weekly_group_correct_picks))

#calculating the number of people who picked this week
Total = dim(inst.picks[[length(weeks)]])[1]
```

```{r plotting group results, include=FALSE}
#Previous Weeks
group_season_for_plotting = unlist(weekly_win_percentage) %>% as.data.frame() %>% 
  rename(`Win Percentage` = ".") %>% 
  add_column(Week = unlist(weeks))
```

```{r Plotting the group results, echo=FALSE}
inst_group_season_plot = group_season_for_plotting %>% 
ggplot(aes(x = as.factor(Week), y = `Win Percentage`))+
  geom_point()+
  geom_path(aes(x = Week))+
  ylim(c(0, 1)) +
  xlab("NFL Week") + 
  ylab("Correct Percentage")+
  ggtitle("Weekly Group Correct Percentage")+
  theme_classic()+
  theme(plot.title = element_text(hjust = 0.5, size = 18))
```

```{r beating cbs week, include=FALSE}
#Creating a list of correct percentages for each week.
cbs_weekly_percent = map(weeks, cbs_percent)

#Creating a list of how many cbs experts we beat each week.
cbs_experts_beat = map2(cbs_weekly_percent, weekly_win_percentage, experts_beat)

#Creating a list of how many cbs experts picked each week.  
cbs_experts_total = map(cbs_weekly_percent, experts_tot)
```

```{r beating cbs season, include=FALSE}
#Creating a list of correct percentages for each week.
cbs_season_percent = map(weeks, cbs_season_percent)

#Creating a list of how many cbs experts we beat each week.
cbs_experts_beat_season = map2(cbs_season_percent, season_win_percentage, experts_beat)

#Creating a list of how many cbs experts picked each week.  
cbs_experts_season_total = map(cbs_season_percent, experts_tot)
```

```{r beating ESPN week, include=FALSE}
#Creating a list of correct percentages for each week.
espn_weekly_percent = map(weeks, espn_percent)

#Creating a list of how many cbs experts we beat each week.
espn_experts_beat = map2(espn_weekly_percent, weekly_win_percentage, experts_beat)

#Creating a list of how many cbs experts picked each week.  
espn_experts_total = map(espn_weekly_percent, experts_tot)
```

```{r beating ESPN season, include=FALSE}
#Creating a list of correct percentages for each week.
espn_season_percent = map(weeks, espn_season_percent)

#Creating a list of how many cbs experts we beat each week.
espn_experts_beat_season = map2(espn_season_percent, season_win_percentage, experts_beat)

#Creating a list of how many cbs experts picked each week.  
espn_experts_season_total = map(espn_season_percent, experts_tot)
```

```{r individual results, include=FALSE}
#Creating a list of individual results for each week.
weekly_indiv = pmap(list(inst.picks, winners, weeks), indiv_weekly_pred)

#Combining each week into one dataframe and calculating percentage Correct for this week.  
full_season = weekly_indiv %>% reduce(full_join, by = "Name") %>% 
  mutate(Percent = round(pull(.[,ncol(.)]/weekly_number_of_games[[length(weekly_number_of_games)]]),4)) 

#Creating a dataframe with only the weekly picks
a = full_season %>% select(starts_with("Week"))

#Creating a vector of how many weeks each person picked over the season
tot_week = NULL
help = NULL
for (i in 1:dim(a)[1]){
  for(j in 1:length(a)){
    help[j] = ifelse(is.na(a[i,j])==T,0,1)
    tot_week[i] = sum(help)
  }
}

#Creating a vector of how many games each person picked over the season
tot_picks= NULL
help = NULL
for (i in 1:dim(a)[1]){
  for(j in 1:length(a)){
    help[j] = unlist(weekly_games_picked)[j]*ifelse(is.na(a[i,j])==T,0,1)
    tot_picks[i] = sum(help)
  }
}

#Creatign a vector of how many games each person picked correct over the season
tot_correct = NULL
help = NULL
for (i in 1:dim(a)[1]){
  tot_correct[i] = sum(a[i,], na.rm = T)
}

#adding how many weeks each person picked, season correct percentage, and adjusted season percentag to the data frame and sorting the data
indiv_disp = full_season %>% add_column(`Weeks Picked` = tot_week) %>%
  add_column(tot_correct)%>%
  add_column(tot_picks)%>%
  mutate(`Season Percent` = round(tot_correct/tot_picks,4))%>%
  mutate(`Adj Season Percent` = round(`Season Percent`*(tot_week/length(a)),4)) %>%
  select(-tot_correct, -tot_picks) %>%
  arrange(desc(Percent), desc(`Season Percent`)) %>%
  mutate(Percent = ifelse(is.na(Percent)==T, 0, Percent))
```


```{r individual percentages, include=FALSE}
#Calculating individual percentages for each week.
weekly_indiv_percent = map2(weekly_indiv, as.list(weekly_number_of_games), indiv_percent) %>% reduce(full_join, by = "Name")

weekly_indiv_percent_plot = weekly_indiv_percent %>% 
  pivot_longer(cols = starts_with("Week"), names_to = "Week", values_to = "Percent")%>%
  mutate(Percent = ifelse(is.na(Percent)==T, 0, Percent)) %>% 
  mutate(Week = as.factor(Week))

levels = NULL
for(i in 1:length(weeks)){
  levels[i] = glue("Week {i}")  
}

weekly_indiv_percent_plot = weekly_indiv_percent_plot %>%
  mutate(Week = factor(Week, levels))
```

```{r sparklines, include=FALSE}
#adding sparklines
plot_group = function(name, df){
  plot_object = 
    ggplot(data = df,
           aes(x = as.factor(Week), y=Percent, group = 1))+
    geom_path(size = 7)+
    scale_y_continuous(limits = c(0,1))+
    theme_void()+
    theme(legend.position = "none")
  return(plot_object)
}

sparklines = 
  weekly_indiv_percent_plot %>% 
  group_by(Name) %>% 
  nest() %>% 
  mutate(plot = map2(Name, data, plot_group)) %>% 
  select(-data)
  
indiv_disp_2 = indiv_disp %>% 
  inner_join(sparklines, by = "Name") %>% 
  mutate(`Season Trend` = NA)
```

```{r Printing Individual Table2, echo=FALSE}
# Printing the individual Table
indiv_table = indiv_disp_2 %>% gt() %>% 
  cols_align(
    align = "center") %>% 
   tab_header(
    title = md("Individual Results"),
    subtitle = md(glue("Week {length(weeks)}"))
    ) %>% 
   tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(Percent),
      rows = Percent<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(Percent),
      rows = Percent>.5
    )) %>% 
     tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`>.5
    ))%>% 
     tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`>.5
    )) %>% 
  tab_options(
    container.width = pct(100),
    data_row.padding = px(1),
    container.height = "100%"
   ) %>%
    tab_spanner(
    label = "Weekly # Correct",
    columns = starts_with(c("Week "))
  ) %>% 
  text_transform(
    locations = cells_body(c(`Season Trend`)),
    fn = function(x){
      map(indiv_disp_2$plot, ggplot_image, height = px(30), aspect_ratio = 4)
                 }) %>%
  cols_hide(c(plot))

indiv_winners = indiv_disp_2 %>% filter(Percent == max(Percent)) %>% select(Name) %>% pull() %>% paste(collapse = ", ")
indiv_season = indiv_disp_2 %>% filter(`Season Percent` == max(`Season Percent`)) %>% select(Name) %>% pull() %>% paste(collapse = ", ")
indiv_season_adj = indiv_disp_2 %>% filter(`Adj Season Percent` == max(`Adj Season Percent`)) %>% select(Name) %>% pull()%>% paste(collapse = ", ")
```

```{r Printing Season Leaderboard, echo=FALSE}
# Printing the Season Leaderboard
  
season_leaderboard_disp = indiv_disp_2 %>% select(Name, starts_with("Week ")) %>% 
  pivot_longer(starts_with("Week"),names_to = "Week", values_to = "Correct") %>% 
  group_by(Week) %>% 
  mutate(Correct = case_when(is.na(Correct)==T~0, 
                             TRUE~Correct)) %>% 
  mutate(Donut = case_when(Correct==max(Correct)~1,
                           TRUE~0))  %>% 
  ungroup() %>% 
  group_by(Name) %>% 
  summarise(`Donuts Won` = sum(Donut)) %>% 
  #mutate(`Donuts Won` = strrep("award,", Donuts)) %>% 
  right_join(.,indiv_disp_2) %>% 
  select(-starts_with("Week "), -Percent) %>% 
  mutate(`Season Rank` = min_rank(desc(`Season Percent`)),.before = Name) %>% 
  arrange(`Season Rank`) 
  
season_leaderboard = season_leaderboard_disp %>% 
  gt() %>% 
  cols_align(
    align = "center") %>% 
   tab_header(
    title = md("Season Leaderboard (Season Percent)"),
    subtitle = md(glue("Week {length(weeks)}"))
    ) %>% 
  # fmt_icon(
  #   columns = `Donuts Won`,
  #   fill_color = "gold",
  # ) %>%
  tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`>.5
    ))%>% 
     tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`>.5
    )) %>% 
  tab_options(
    container.width = pct(100),
    data_row.padding = px(1),
    container.height = "100%"
   ) %>%
    tab_spanner(
    label = "Weekly # Correct",
    columns = starts_with(c("Week "))
  ) %>% 
  text_transform(
    locations = cells_body(c(`Season Trend`)),
    fn = function(x){
      map(season_leaderboard_disp$plot, ggplot_image, height = px(30), aspect_ratio = 4)
                 }) %>%
  cols_hide(columns = c(plot))
```

```{r Printing Adj Season Leaderboard, echo=FALSE}
# Printing the Adj Season Leaderboard
  
adj_season_leaderboard_disp = indiv_disp_2 %>% select(Name, starts_with("Week ")) %>% 
  pivot_longer(starts_with("Week"),names_to = "Week", values_to = "Correct") %>% 
  group_by(Week) %>% 
  mutate(Correct = case_when(is.na(Correct)==T~0, 
                             TRUE~Correct)) %>% 
  mutate(Donut = case_when(Correct==max(Correct)~1,
                           TRUE~0))  %>% 
  ungroup() %>% 
  group_by(Name) %>% 
  summarise(`Donuts Won` = sum(Donut)) %>% 
  #mutate(`Donuts Won` = strrep("award,", Donuts)) %>% 
  right_join(.,indiv_disp_2) %>% 
  select(-starts_with("Week "), -Percent) %>% 
  mutate(`Season Rank` = min_rank(desc(`Adj Season Percent`)),.before = Name) %>% 
  arrange(`Season Rank`)

adj_season_leaderboard = adj_season_leaderboard_disp %>% 
  gt() %>% 
  cols_align(
    align = "center") %>% 
   tab_header(
    title = md("Season Leaderboard (Adjusted Season Percent)"),
    subtitle = md(glue("Week {length(weeks)}"))
    ) %>% 
  # fmt_icon(
  #   columns = `Donuts Won`,
  #   fill_color = "gold",
  # ) %>%
  tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Season Percent`),
      rows = `Season Percent`>.5
    ))%>% 
     tab_style(
    style = cell_text(color = "red", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`<.5
    )) %>% 
   tab_style(
    style = cell_text(color = "green", weight = "bold"),
    locations = cells_body(
      columns = c(`Adj Season Percent`),
      rows = `Adj Season Percent`>.5
    )) %>% 
  tab_options(
    container.width = pct(100),
    data_row.padding = px(1),
    container.height = "100%"
   ) %>%
    tab_spanner(
    label = "Weekly # Correct",
    columns = starts_with(c("Week "))
  ) %>% 
  text_transform(
    locations = cells_body(c(`Season Trend`)),
    fn = function(x){
      map(adj_season_leaderboard_disp$plot, ggplot_image, height = px(30), aspect_ratio = 4)
                 }) %>%
  cols_hide(columns = c(plot))
```


```{r instructor formattable, echo=FALSE}
improvement_formatter <- 
  formatter("span", 
            style = x ~ formattable::style(
              font.weight = "bold", 
              color = ifelse(x > .5, "green", ifelse(x < .5, "red", "black"))),
             x ~ icontext(ifelse(x == max(x), "star", ""), x))

indiv_disp_3 = indiv_disp_2 %>% select(-plot)
indiv_disp_3$`Season Trend` = apply(indiv_disp_3[,2:(1+length(weeks))], 1, FUN = function(x) as.character(htmltools::as.tags(sparkline(as.numeric(x), type = "line", chartRangeMin = 0, chartRangeMax = 1, fillColor = "white"))))

indiv_table_2 = as.htmlwidget(formattable(indiv_disp_3, 
                                align = c("l", rep("c", NROW(indiv_disp_3)-1)),
              list(`Season Percent` = color_bar("#FA614B"),
              `Season Percent`= improvement_formatter,
              `Adj Season Percent`= improvement_formatter)))
              
indiv_table_2$dependencies = c(indiv_table_2$dependencies, htmlwidgets:::widget_dependencies("sparkline", "sparkline"))
```

```{r Plotting individual results over the season2, eval=FALSE, include=FALSE, out.width="100%"}
#Creating the individual plot.  
inst_indiv_plots = weekly_indiv_percent_plot %>% 
  ggplot(aes(x = factor(Week), y = Percent, color = Name))+
  geom_point()+
  geom_path(aes(x = as.factor(Week), y = Percent, color = Name, 
                group = Name))+
  ylim(c(0, 1)) +
  labs(x = "NFL Week", 
       y = "Correct Percentage", 
       title = "Weekly Individual Correct Percentage")+
  facet_wrap(~Name)+
  theme_classic()+
  theme(legend.position = "none",
        plot.title = element_text(hjust = 0.5, size = 18),
        axis.text.x=element_text(angle =45, vjust = 1, hjust = 1))
```

```{r data for data page}
inst.data = map2(inst.picks, weeks, disp_data) %>% bind_rows()
```


```{r fivethirtyeight}
inst_538 = map(results, five38) %>% unlist() %>% sum()
```

```{r pregame, eval=FALSE, include=FALSE}
#Predictions for the week

#Creating the list of group predictions each week.
games = map(inst.picks, games_fn)

#Creating the prediction table.  
pred_table = map(games, pred_table_fn)

#Printing table of instructor predictions
pred_table[[length(pred_table)]] %>% mutate(Game = row_number()) %>% 
  rename(`Votes For` = votes_for, `Votes Against` = votes_against) %>% 
  gt() %>% 
  cols_align(
    align = "center") %>% 
   tab_header(
    title = md("This Week's Predictions"),
    subtitle = md(glue("Week {length(weeks)}"))
    ) %>% 
   tab_options(
    data_row.padding = px(3)
   )
```

Group Predictions
==========================================================================

Sidebar {.sidebar} 
-------------------------------------
#### CBS Sports

<font size="4">

This week we beat or tied `r cbs_experts_beat[[length(weeks)]]` of `r cbs_experts_total[[length(weeks)]]` CBS Sports' Experts.

For the season we are currently beating or tied with `r cbs_experts_beat_season[[length(weeks)]]` of `r cbs_experts_season_total[[length(weeks)]]` CBS Sports' Experts.
 
 </font>


#### ESPN

<font size="4">

We also beat or tied `r espn_experts_beat[[length(weeks)]]` of `r espn_experts_total[[length(weeks)]]` ESPN Experts.
 
For the season we are currently beating or tied with `r espn_experts_beat_season[[length(weeks)]]` of `r espn_experts_season_total[[length(weeks)]]` ESPN Experts.

</font>

Row
--------------------------------------

### Win percentage for the week

```{r}
inst_rate <- weekly_win_percentage[[length(weekly_win_percentage)]]*100
gauge(inst_rate, min = 0, max = 100, symbol = '%', gaugeSectors(
  success = c(55, 100), warning = c(40, 54), danger = c(0, 39)
))
```

### Season Win Percentage

```{r}
inst_season <- season_win_percentage*100
gauge(inst_season, min = 0, max = 100, symbol = '%', gaugeSectors(
  success = c(55, 100), warning = c(40, 54), danger = c(0, 39)
))
```

### Games Correct
```{r}
valueBox(value = season_wins,icon = "fa-trophy",caption = "Correct Games this Season")
```

### Games Picked
```{r}
valueBox(value = season_games,icon = "fa-clipboard-list",caption = "Games Picked this Season")
```

### Number of predictions
```{r}
valueBox(value = Total,icon = "fa-users",caption = "Predictions this week")
```

Row
--------------------------------------

### 

```{r}
inst_group_table
```

### 

```{r}
ggplotly(inst_group_season_plot) %>% 
  layout(title = list(y = .93, xref = "plot"),
         margin = list(t = 40))
```

Individual Predictions
==========================================================================


Sidebar {.sidebar} 
-------------------------------------

#### Best Picks of the Week.

<font size="4">

 `r indiv_winners`
 
 </font>
 
#### Best Season Correct Percentage
<font size="4">

`r indiv_season`
 
 </font>

#### Best Adjusted Season Correct Percentage
<font size="4">

`r indiv_season_adj`

 * Adjusted season percentage accounts for the number of weeks picked.
 
 </font>

row {.tabset}
--------------------------------------

### Individual Table
```{r}
indiv_table
```

<!--
### Individual Table2

```{r, out.height="100%"}
indiv_table_2
```

-->

<!--

### Individual Plots
```{r, out.width="100%"}
#ggplotly(inst_indiv_plots)
```

-->

### Season Leaderboard
```{r, out.width="100%"}
season_leaderboard
```

### Adjusted Season Leaderboard
```{r, out.width="100%"}
adj_season_leaderboard
```

Data
==========================================================================

```{r}
datatable(
  inst.data, extensions = 'Buttons', options = list(
    dom = 'Blfrtip',
    buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
    lengthMenue = list( c(10, 25, 50, 100, -1), c(10, 25, 50, 100, "All") )
  )
)
```